personal finance AGI is upon us, and as this paper demonstrates, will lift all boats! and the results of this experiment would be even stronger if ran today. this experiment ran on GPT 5.2. OpenAI's Personal Finance benchmark, shared around the time of 5.5, demonstrated a huge improvement over 5.3. now we're onto 5.6, no external benchmarks to share, but I promise you it's better. we still need to do a better job though, helping users ask better questions, so they get the best answers! 🫡 ON IT!

Now in research preview: Codex Security Review It takes a deeper look at GitHub pull requests for security issues, using repository context to surface actionable findings directly in the PR. See how to enable automatic reviews:

Luna is such an insane value after the 80% cost reduction. It's basically free and can do a ton of real data processing type work. I'm overhauling title generation in T3 Code to take more advantage of it. I almost want to spin it up on every prompt to generate descriptions, feedback, and statuses. Why wouldn't I? It's basically free

GPT-Live can listen while it speaks. To make that feel natural at ChatGPT scale, we rebuilt the voice stack from client to model. This new architecture keeps audio flowing continuously, so deeper reasoning and tool use don't interrupt the conversation.

I let OpenAI's Codex app run ads for my business. It cut the video, published it, built the audience, set the budget then stopped at the Pay button and asked permission. The part that gets me: I watched the whole thing happen live inside the app.

CODEX SKILL THAT TURNS CUSTOMER FEEDBACK INTO A ROADMAP! Most feedback analysis stops at positive or negative. I made a Codex skill that turns support tickets, interviews, surveys, reviews, sales calls, and churn notes into evidence-backed product priorities and credible customer proof. Give it your feedback files and Codex maps recurring pains, ranks what to build next, and links every recommendation back to exact evidence IDs. It keeps four things separate: -what customers said -the underlying problem -what to validate or build next -what you can honestly claim -> feedback clustering with source traceability -> product opportunity scoring -> Now / Next / Later roadmap -> customer proof + case-study candidates -> objections, churn risks, and unsupported claims -> searchable Startup Customer Evidence Map -> privacy-first quote and consent handling -> one-command install What would you analyze first: support tickets, interviews, or churn notes? Install: npx --yes startup-feedback-engine@latest 100% open source. Repo in Bio.

Only if education could be this interactive ❤️‍🔥 I've had a looong wish to build something genuinely useful through vibe coding, and I finally did it. A 3D human anatomy application built with @threejs using GPT 5.6 Sol. It all started with a single design image that I created using GPT Image 2.0. I then used it to generate every 3D organ image, one by one. Next, I converted each of those images into 3D models using @tripoai (and no, they didn't sponsor this 😄). After that, I opened Codex, wrote a master prompt based on the design, and gave it the prompt, the design image, and all the 3D models. Codex built the first version beautifully, but there was one big problem. Every single 3D model was nearly 120-150 MB. That obviously wasn't practical for the web and was giving a performance of 16fps. After a few iterations, Codex optimized each model down to roughly 2–5.5 MB while preserving the visual quality, reducing the total asset size from ~900 MB to just 28.6 MB. And each model loads on demand. Along the way, Codex also generated those anatomical illustrations showing where each organ sits in the human body, and even created the interactive hotspot markers that explain different parts of every organ. It handled all of that. The process wasn't exactly one shot, but it also wasn't difficult. You just have to do it step by step. It genuinely felt like building something that could make learning anatomy much more engaging. The inspiration came from @DilumSanjaya's 3D animal plant cell project. I remember seeing it and thinking, "I want to build something like this one day." And I did it :D Live: Code:

my dad using gpt 5.6 sol for the 1st time: - wanted to build a webpage about indigenous south african fynbos (extreme plant dad) - read all of the thinking states as it streamed - final thoughts on the HTML file: “its gorgeous, its absolutely gorgeous”

yes, nonsofic groups exist: this statement is one of many new beautiful results proved by Astra, our next major model. We're releasing 10 such Astra proofs, complete with lean certificates and CoT walkthroughs for each of them. The results are wide-ranging, from von Neumann algebras (disproof of Connes' Rigidity Conjecture) to better bounds for high dimensional sphere packing, for circuit complexity, for monochromatic triangles in multicolored graphs, and more. More thoughts here:

happy friday we've continued our work on git here at @openai. performance, correctness, & testing improvements are flowing from openai/git to upstream. perf improvements in codex app too; you'll see more efficient use of git in new builds. follow along: